Modern engines have an increasing number of sensors and actuators to achieve improved control. For example, an engine may incorporate Continuously Variable Valve Lift (CVVL), along with intake and exhaust valve phasing, to control the air charge introduced into a cylinder, effectively performing the function traditionally performed by the throttle valve while lowering pumping losses associated with a throttle valve. While these newer strategies offer the opportunity for improved engine control to achieve performance, efficiency, and emissions goals, the additional degrees of freedom introduced by the additional actuators complicate the engine calibration process. As the number of actuators increases, it becomes impractical to directly map the response of various engine parameters to all possible combinations of actuator settings. Even if such maps could be developed, the memory and computation requirements for the requisite look-up tables make real-time control a challenge.
As an alternative to direct mapping, a model-based approach has been proposed. In this approach, engine responses are measured over ranges of control factors, and the collected data is used to formulate a mathematical model representing the engine response to the control factors. The resulting model may be in the form of an equation that relates the engine response to the control factors. The model represents a statistical fit of the collected data, and while it may represent the general shape of the response surface relating the engine response to the control factors, the absolute accuracy of the model may be insufficient at some engine operating points.
It is desirable to provide a method for determining engine response characteristics that provides improved accuracy with lower computational demands.